Imperial College London

Dr Zulma M Cucunubá

Faculty of MedicineSchool of Public Health

MRC/Rutherford Fund Research Fellow
 
 
 
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Contact

 

zulma.cucunuba

 
 
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Location

 

G27Medical SchoolSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
to

79 results found

Nouvellet P, Bhatia S, Cori A, Ainslie K, Baguelin M, Bhatt S, Boonyasiri A, Brazeau N, Cattarino L, Cooper L, Coupland H, Cucunuba Perez Z, Cuomo-Dannenburg G, Dighe A, Djaafara A, Dorigatti I, Eales O, van Elsland S, NASCIMENTO F, Fitzjohn R, Gaythorpe K, Geidelberg L, green W, Hamlet A, Hauck K, Hinsley W, Imai N, Jeffrey, Jeffrey B, Knock E, Laydon D, Lees J, Mangal T, Mellan T, Nedjati Gilani G, Parag K, Pons Salort M, Ragonnet-Cronin M, Riley S, Unwin H, Verity R, Vollmer M, Volz E, Walker P, Walters C, Wang H, Watson O, Whittaker C, Whittles L, Xi X, Ferguson N, Donnelly Cet al., 2021, Reduction in mobility and COVID-19 transmission, Nature Communications, ISSN: 2041-1723

In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts.Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world.Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27-77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49-91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12-48%]) post-relaxation.In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.

Journal article

Unwin H, Mishra S, Bradley V, Gandy A, Mellan T, Coupland H, Ish-Horowicz J, Vollmer M, Whittaker C, Filippi S, Xi X, Monod M, Ratmann O, Hutchinson M, Valka F, Zhu H, Hawryluk I, Milton P, Ainslie K, Baguelin M, Boonyasiri A, Brazeau N, Cattarino L, Cucunuba Z, Cuomo-Dannenburg G, Dorigatti I, Eales O, Eaton J, van Elsland S, Fitzjohn R, Gaythorpe K, Green W, Hinsley W, Jeffrey B, Knock E, Laydon D, Lees J, Nedjati-Gilani G, Nouvellet P, Okell L, Parag K, Siveroni I, Thompson H, Walker P, Walters C, Watson O, Whittles L, Ghani A, Ferguson N, Riley S, Donnelly C, Bhatt S, Flaxman Set al., 2020, State-level tracking of COVID-19 in the United States, Nature Communications, ISSN: 2041-1723

As of 1st June 2020, the US Centers for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available deathdata within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on therate of transmission of SARS-CoV-2. We estimate thatRtwas only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.

Journal article

Laiton-Donato K, Villabona-Arenas CJ, Usme-Ciro JA, Franco-Muñoz C, Álvarez-Díaz DA, Villabona-Arenas LS, Echeverría-Londoño S, Cucunubá ZM, Franco-Sierra ND, Flórez AC, Ferro C, Ajami NJ, Walteros DM, Prieto F, Durán CA, Ospina-Martínez ML, Mercado-Reyes Met al., 2020, Genomic Epidemiology of Severe Acute Respiratory Syndrome Coronavirus 2, Colombia., Emerg Infect Dis, Vol: 26, Pages: 2854-2862

Coronavirus disease (COVID-19) in Colombia was first diagnosed in a traveler arriving from Italy on February 26, 2020. However, limited data are available on the origins and number of introductions of COVID-19 into the country. We sequenced the causative agent of COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), from 43 clinical samples we collected, along with another 79 genome sequences available from Colombia. We investigated the emergence and importation routes for SARS-CoV-2 into Colombia by using epidemiologic, historical air travel, and phylogenetic observations. Our study provides evidence of multiple introductions, mostly from Europe, and documents >12 lineages. Phylogenetic findings validate the lineage diversity, support multiple importation events, and demonstrate the evolutionary relationship of epidemiologically linked transmission chains. Our results reconstruct the early evolutionary history of SARS-CoV-2 in Colombia and highlight the advantages of genome sequencing to complement COVID-19 outbreak investigations.

Journal article

Thompson H, Imai N, Dighe A, Ainslie K, Baguelin M, Bhatia S, Bhatt S, Boonyasiri A, Boyd O, Brazeau N, Cattarino L, Cooper L, Coupland H, Cucunuba Z, Cuomo-Dannenburg G, Djaafara B, Dorigatti I, van Elsland S, Fitzjohn R, Fu H, Gaythorpe K, Green W, Hallett T, Hamlet A, Haw D, Hayes S, Hinsley W, Jeffrey B, Knock E, Laydon D, Lees J, Mangal T, Mellan T, Mishra S, Mousa A, Nedjati-Gilani G, Nouvellet P, Okell L, Parag K, Ragonnet-Cronin M, Riley S, Unwin H, Verity R, Vollmer M, Volz E, Walker P, Walters C, Wang H, Wang Y, Watson O, Whittaker C, Whittles L, Winskill P, Xi X, Donnelly C, Ferguson Net al., 2020, SARS-CoV-2 infection prevalence on repatriation flights from Wuhan City, China, Journal of Travel Medicine, Vol: 27, Pages: 1-3, ISSN: 1195-1982

We estimated SARS-CoV-2 infection prevalence in cohorts of repatriated citizens from Wuhan to be 0.44% (95% CI: 0.19%–1.03%). Although not representative of the wider population we believe these estimates are helpful in providing a conservative estimate of infection prevalence in Wuhan City, China, in the absence of large-scale population testing early in the epidemic.

Journal article

Grassly NC, Pons-Salort M, Parker EPK, White PJ, Ferguson NM, Imperial College COVID-19 Response Teamet al., 2020, Comparison of molecular testing strategies for COVID-19 control: a mathematical modelling study, Lancet Infectious Diseases, Vol: 20, Pages: 1381-1389, ISSN: 1473-3099

BACKGROUND: WHO has called for increased testing in response to the COVID-19 pandemic, but countries have taken different approaches and the effectiveness of alternative strategies is unknown. We aimed to investigate the potential impact of different testing and isolation strategies on transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We developed a mathematical model of SARS-CoV-2 transmission based on infectiousness and PCR test sensitivity over time since infection. We estimated the reduction in the effective reproduction number (R) achieved by testing and isolating symptomatic individuals, regular screening of high-risk groups irrespective of symptoms, and quarantine of contacts of laboratory-confirmed cases identified through test-and-trace protocols. The expected effectiveness of different testing strategies was defined as the percentage reduction in R. We reviewed data on the performance of antibody tests reported by the Foundation for Innovative New Diagnostics and examined their implications for the use of so-called immunity passports. FINDINGS: If all individuals with symptoms compatible with COVID-19 self-isolated and self-isolation was 100% effective in reducing onwards transmission, self-isolation of symptomatic individuals would result in a reduction in R of 47% (95% uncertainty interval [UI] 32-55). PCR testing to identify SARS-CoV-2 infection soon after symptom onset could reduce the number of individuals needing to self-isolate, but would also reduce the effectiveness of self-isolation (around 10% would be false negatives). Weekly screening of health-care workers and other high-risk groups irrespective of symptoms by use of PCR testing is estimated to reduce their contribution to SARS-CoV-2 transmission by 23% (95% UI 16-40), on top of reductions achieved by self-isolation following symptoms, assuming results are available at 24 h. The effectiveness of test and trace depends strongly on coverage and the timelines

Journal article

Biggerstaff M, Cowling BJ, Cucunubá ZM, Dinh L, Ferguson NM, Gao H, Hill V, Imai N, Johansson MA, Kada S, Morgan O, Pastore y Piontti A, Polonsky JA, Prasad PV, Quandelacy TM, Rambaut A, Tappero JW, Vandemaele KA, Vespignani A, Warmbrod KL, Wong JYet al., 2020, Early insights from statistical and mathematical modeling of key epidemiologic parameters of COVID-19, Emerging Infectious Diseases, Vol: 26, ISSN: 1080-6040

We report key epidemiologic parameter estimates for coronavirus disease identified in peer-reviewed publications, preprint articles, and online reports. Range estimates for incubation period were 1.8–6.9 days, serial interval 4.0–7.5 days, and doubling time 2.3–7.4 days. The effective reproductive number varied widely, with reductions attributable to interventions. Case burden and infection fatality ratios increased with patient age. Implementation of combined interventions could reduce cases and delay epidemic peak up to 1 month. These parameters for transmission, disease severity, and intervention effectiveness are critical for guiding policy decisions. Estimates will likely change as new information becomes available.

Journal article

Cucunubá ZM, 2020, Latin American scientific research prorities for COVID-19 prevention and control [Investigación científica prioritaria en Latinoamérica para orientar la prevención y el control de la COVID-19 ], Biomedica, Vol: 40, Pages: 9-13, ISSN: 0120-4157

Journal article

Brazeau N, Verity R, Jenks S, Fu H, Whittaker C, Winskill P, Dorigatti I, Walker P, Riley S, Schnekenberg RP, Heltgebaum H, Mellan T, Mishra S, Unwin H, Watson O, Cucunuba Perez Z, Baguelin M, Whittles L, Bhatt S, Ghani A, Ferguson N, Okell Let al., 2020, Report 34: COVID-19 infection fatality ratio: estimates from seroprevalence

The infection fatality ratio (IFR) is a key statistic for estimating the burden of coronavirus disease 2019 (COVID-19) and has been continuously debated throughout the current pandemic. Previous estimates have relied on data early in the epidemic, or have not fully accounted for uncertainty in serological test characteristics and delays from onset of infection to seroconversion, death, and antibody waning. After screening 175 studies, we identified 10 representative antibody surveys to obtain updated estimates of the IFR using a modelling framework that addresses the limitations listed above. We inferred serological test specificity from regional variation within serosurveys, which is critical for correctly estimating the cumulative proportion infected when seroprevalence is still low. We find that age-specific IFRs follow an approximately log-linear pattern, with the risk of death doubling approximately every eight years of age. Using these age-specific estimates, we estimate the overall IFR in a typical low-income country, with a population structure skewed towards younger individuals, to be 0.23% (0.14-0.42 95% prediction interval range). In contrast, in a typical high income country, with a greater concentration of elderly individuals, we estimate the overall IFR to be 1.15% (0.78-1.79 95% prediction interval range). We show that accounting for seroreversion, the waning of antibodies leading to a negative serological result, can slightly reduce the IFR among serosurveys conducted several months after the first wave of the outbreak, such as Italy. In contrast, uncertainty in test false positive rates combined with low seroprevalence in some surveys can reconcile apparently low crude fatality ratios with the IFR in other countries. Unbiased estimates of the IFR continue to be critical to policymakers to inform key response decisions. It will be important to continue to monitor the IFR as new treatments are introduced. The code for reproducing these results are av

Report

Carrera J-P, Cucunubá ZM, Neira K, Lambert B, Pittí Y, Liscano J, Garzón JL, Beltran D, Collado-Mariscal L, Saenz L, Sosa N, Rodriguez-Guzman LD, González P, Lezcano AG, Pereyra-Elías R, Valderrama A, Weaver SC, Vittor AY, Armién B, Pascale J-M, Donnelly CAet al., 2020, Endemic and epidemic human alphavirus infections in eastern Panama: an analysis of population-based cross-sectional surveys., American Journal of Tropical Medicine and Hygiene, Pages: 1-22, ISSN: 0002-9637

Madariaga virus (MADV) has recently been associated with severe human disease in Panama, where the closely related Venezuelan equine encephalitis virus (VEEV) also circulates. In June 2017, a fatal MADV infection was confirmed in a community of Darien Province. We conducted a cross-sectional outbreak investigation with human and mosquito collections in July 2017, where sera were tested for alphavirus antibodies and viral RNA. In addition, by applying a catalytic, force-of-infection (FOI) statistical model to two serosurveys from Darien Province in 2012 and 2017, we investigated whether endemic or epidemic alphavirus transmission occurred historically. In 2017, MADV and VEEV IgM seroprevalences were 1.6% and 4.4%, respectively; IgG antibody prevalences were MADV: 13.2%, VEEV: 16.8%, Una virus (UNAV): 16.0%, and Mayaro virus: 1.1%. Active viral circulation was not detected. Evidence of MADV and UNAV infection was found near households, raising questions about its vectors and enzootic transmission cycles. Insomnia was associated with MADV and VEEV infections, depression symptoms were associated with MADV, and dizziness with VEEV and UNAV. Force-of-infection analyses suggest endemic alphavirus transmission historically, with recent increased human exposure to MADV and VEEV in Aruza and Mercadeo, respectively. The lack of additional neurological cases suggests that severe MADV and VEEV infections occur only rarely. Our results indicate that over the past five decades, alphavirus infections have occurred at low levels in eastern Panama, but that MADV and VEEV infections have recently increased-potentially during the past decade. Endemic infections and outbreaks of MADV and VEEV appear to differ spatially in some locations of eastern Panama.

Journal article

Fu H, Wang H, Xi X, Boonyasiri A, Wang Y, Hinsley W, Fraser KJ, McCabe R, Olivera Mesa D, Skarp J, Ledda A, Dewé T, Dighe A, Winskill P, van Elsland SL, Ainslie KEC, Baguelin M, Bhatt S, Boyd O, Brazeau NF, Cattarino L, Charles G, Coupland H, Cucunubá ZM, Cuomo-Dannenburg G, Donnelly CA, Dorigatti I, Eales OD, Fitzjohn RG, Flaxman S, Gaythorpe KAM, Ghani AC, Green WD, Hamlet A, Hauck K, Haw DJ, Jeffrey B, Laydon DJ, Lees JA, Mellan T, Mishra S, Nedjati Gilani G, Nouvellet P, Okell L, Parag KV, Ragonnet-Cronin M, Riley S, Schmit N, Thompson HA, Unwin HJT, Verity R, Vollmer MAC, Volz E, Walker PGT, Walters CE, Waston OJ, Whittaker C, Whittles LK, Imai N, Bhatia S, Ferguson NMet al., 2020, A database for the epidemic trends and control measures during the first wave of COVID-19 in mainland China, International Journal of Infectious Diseases, ISSN: 1201-9712

Objectives: This data collation effort aims to provide a comprehensive database to describe the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19)across main provinces in China. Methods: From mid-January to March 2020, we extracted publicly available data on the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted a descriptive analysis of the epidemics in the six most-affected provinces. Results: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends were different across provinces. Compared to Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as local transmission of COVID-19 declined, switching the focus of measures to testing and quarantine of inbound travellers could help to sustain the control of the epidemic. Conclusions: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database with these indicators and information on control measures provides useful source for exploring further research and policy planning for response to the COVID-19 epidemic.

Journal article

Dighe A, Cattarino L, Cuomo-Dannenburg G, Skarp J, Imai N, Bhatia S, Gaythorpe K, Ainslie K, Baguelin M, Bhatt S, Boonyasiri A, Brazeau N, Cooper L, Coupland H, Cucunuba Perez Z, Dorigatti I, Eales O, van Elsland S, Fitzjohn R, Green W, Haw D, Hinsley W, Knock E, Laydon D, Mellan T, Mishra S, Nedjati Gilani G, Nouvellet P, Pons Salort M, Thompson H, Unwin H, Verity R, Vollmer M, Walters C, Watson O, Whittaker C, Whittles L, Ghani A, Donnelly C, Ferguson N, Riley Set al., 2020, Response to COVID-19 in South Korea and implications for lifting stringent interventions, BMC Medicine, Vol: 18, Pages: 1-12, ISSN: 1741-7015

Background After experiencing a sharp growth in COVID-19 cases early in the pandemic, South Korea rapidly controlled transmission while implementing less stringent national social distancing measures than countries in Europe and the US. This has led to substantial interest in their “test, trace, isolate” strategy. However, it is important to understand the epidemiological peculiarities of South Korea’s outbreak and characterise their response before attempting to emulate these measures elsewhere.MethodsWe systematically extracted numbers of suspected cases tested, PCR-confirmed cases, deaths, isolated confirmed cases, and numbers of confirmed cases with an identified epidemiological link from publicly available data. We estimated the time-varying reproduction number, Rt, using an established Bayesian framework, and reviewed the package of interventions implemented by South Korea using our extracted data, plus published literature and government sources. Results We estimated that after the initial rapid growth in cases, Rt dropped below one in early April before increasing to a maximum of 1.94 (95%CrI; 1.64-2.27) in May following outbreaks in Seoul Metropolitan Region. By mid-June Rt was back below one where it remained until the end of our study (July 13th). Despite less stringent “lockdown” measures, strong social distancing measures were implemented in high incidence areas and studies measured a considerable national decrease in movement in late-February. Testing capacity was swiftly increased, and protocols were in place to isolate suspected and confirmed cases quickly however we could not estimate the delay to isolation using our data. Accounting for just 10% of cases, individual case-based contact-tracing picked up a relatively minor proportion of total cases, with cluster investigations accounting for 66%. ConclusionsWhilst early adoption of testing and contact-tracing are likely to be important for South Korea’s successf

Journal article

Ainslie KEC, Walters CE, Fu H, Bhatia S, Wang H, Xi X, Baguelin M, Bhatt S, Boonyasiri A, Boyd O, Cattarino L, Ciavarella C, Cucunuba Z, Cuomo-Dannenburg G, Dighe A, Dorigatti I, van Elsland SL, FitzJohn R, Gaythorpe K, Ghani AC, Green W, Hamlet A, Hinsley W, Imai N, Jorgensen D, Knock E, Laydon D, Nedjati-Gilani G, Okell LC, Siveroni I, Thompson HA, Unwin HJT, Verity R, Vollmer M, Walker PGT, Wang Y, Watson OJ, Whittaker C, Winskill P, Donnelly CA, Ferguson NM, Riley Set al., 2020, Evidence of initial success for China exiting COVID-19 social distancing policy after achieving containment, Wellcome Open Research, Vol: 5, Pages: 81-81

<ns4:p><ns4:bold>Background</ns4:bold>: The COVID-19 epidemic was declared a Global Pandemic by WHO on 11 March 2020. By 24 March 2020, over 440,000 cases and almost 20,000 deaths had been reported worldwide. In response to the fast-growing epidemic, which began in the Chinese city of Wuhan, Hubei, China imposed strict social distancing in Wuhan on 23 January 2020 followed closely by similar measures in other provinces. These interventions have impacted economic productivity in China, and the ability of the Chinese economy to resume without restarting the epidemic was not clear.</ns4:p><ns4:p> <ns4:bold>Methods</ns4:bold>: Using daily reported cases from mainland China and Hong Kong SAR, we estimated transmissibility over time and compared it to daily within-city movement, as a proxy for economic activity.</ns4:p><ns4:p> <ns4:bold>Results</ns4:bold>: Initially, within-city movement and transmission were very strongly correlated in the five mainland provinces most affected by the epidemic and Beijing. However, that correlation decreased rapidly after the initial sharp fall in transmissibility. In general, towards the end of the study period, the correlation was no longer apparent, despite substantial increases in within-city movement. A similar analysis for Hong Kong shows that intermediate levels of local activity were maintained while avoiding a large outbreak. At the very end of the study period, when China began to experience the re-introduction of a small number of cases from Europe and the United States, there is an apparent up-tick in transmission.</ns4:p><ns4:p> <ns4:bold>Conclusions:</ns4:bold> Although these results do not preclude future substantial increases in incidence, they suggest that after very intense social distancing (which resulted in containment), China successfully exited its lockdown to some degree. Elsewhere, movement data are being used as proxies for

Journal article

Hogan A, Winskill P, Watson O, Walker P, Whittaker C, Baguelin M, Haw D, Lochen A, Gaythorpe K, Ainslie K, Bhatt S, Boonyasiri A, Boyd O, Brazeau N, Cattarino L, Charles G, Cooper L, Coupland H, Cucunuba Perez Z, Cuomo-Dannenburg G, Donnelly C, Dorigatti I, Eales O, van Elsland S, Ferreira Do Nascimento F, Fitzjohn R, Flaxman S, Green W, Hallett T, Hamlet A, Hinsley W, Imai N, Jauneikaite E, Jeffrey B, Knock E, Laydon D, Lees J, Mellan T, Mishra S, Nedjati Gilani G, Nouvellet P, Ower A, Parag K, Ragonnet-Cronin M, Siveroni I, Skarp J, Thompson H, Unwin H, Verity R, Vollmer M, Volz E, Walters C, Wang H, Wang Y, Whittles L, Xi X, Muhib F, Smith P, Hauck K, Ferguson N, Ghani Aet al., 2020, Report 33: Modelling the allocation and impact of a COVID-19 vaccine

Several SARS-CoV-2 vaccine candidates are now in late-stage trials, with efficacy and safety results expected by the end of 2020. Even under optimistic scenarios for manufacture and delivery, the doses available in 2021 are likely to be limited. Here we identify optimal vaccine allocation strategies within and between countries to maximise health (avert deaths) under constraints on dose supply. We extended an existing mathematical model of SARS-CoV-2 transmission across different country settings to model the public health impact of potential vaccines, using a range of target product profiles developed by the World Health Organization. We show that as supply increases, vaccines that reduce or block infection – and thus transmission – in addition to preventing disease have a greater impact than those that prevent disease alone, due to the indirect protection provided to high-risk groups. We further demonstrate that the health impact of vaccination will depend on the cumulative infection incidence in the population when vaccination begins, the duration of any naturally acquired immunity, the likely trajectory of the epidemic in 2021 and the level of healthcare available to effectively treat those with disease. Within a country, we find that for a limited supply (doses for <20% of the population) the optimal strategy is to target the elderly and other high-risk groups. However, if a larger supply is available, the optimal strategy switches to targeting key transmitters (i.e. the working age population and potentially children) to indirectly protect the elderly and vulnerable. Given the likely global dose supply in 2021 (2 billion doses with a two-dose vaccine), we find that a strategy in which doses are allocated to countries in proportion to their population size is close to optimal in averting deaths. Such a strategy also aligns with the ethical principles agreed in pandemic preparedness planning.

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Monod M, Blenkinsop A, Xi X, Herbert D, Bershan S, Tietze S, Bradley V, Chen Y, Coupland H, Filippi S, Ish-Horowicz J, McManus M, Mellan T, Gandy A, Hutchinson M, Unwin H, Vollmer M, Weber S, Zhu H, Bezancon A, Ferguson N, Mishra S, Flaxman S, Bhatt S, Ratmann O, Ainslie K, Baguelin M, Boonyasiri A, Boyd O, Cattarino L, Cooper L, Cucunuba Perez Z, Cuomo-Dannenburg G, Djaafara A, Dorigatti I, van Elsland S, Fitzjohn R, Gaythorpe K, Geidelberg L, Green W, Hamlet A, Jeffrey B, Knock E, Laydon D, Nedjati Gilani G, Nouvellet P, Parag K, Siveroni I, Thompson H, Verity R, Walters C, Donnelly C, Okell L, Bhatia S, Brazeau N, Eales O, Haw D, Imai N, Jauneikaite E, Lees J, Mousa A, Olivera Mesa D, Skarp J, Whittles Let al., 2020, Report 32: Targeting interventions to age groups that sustain COVID-19 transmission in the United States

Following ini􀀂al declines, in mid 2020, a resurgence in transmission of novel coronavirus disease (COVID-19) has occurred in the United States and parts of Europe. Despite the wide implementa􀀂on of non-pharmaceu􀀂cal inter-ven􀀂ons, it is s􀀂ll not known how they are impacted by changing contact pa􀀁erns, age and other demographics. As COVID-19 disease control becomes more localised, understanding the age demographics driving transmission and how these impact the loosening of interven􀀂ons such as school reopening is crucial. Considering dynamics for the United States, we analyse aggregated, age-specific mobility trends from more than 10 million individuals and link these mechanis􀀂cally to age-specific COVID-19 mortality data. In contrast to previous approaches, we link mobility to mortality via age specific contact pa􀀁erns and use this rich rela􀀂onship to reconstruct accurate trans-mission dynamics. Contrary to anecdotal evidence, we find li􀀁le support for age-shi􀀃s in contact and transmission dynamics over 􀀂me. We es􀀂mate that, un􀀂l August, 63.4% [60.9%-65.5%] of SARS-CoV-2 infec􀀂ons in the United States originated from adults aged 20-49, while 1.2% [0.8%-1.8%] originated from children aged 0-9. In areas with con􀀂nued, community-wide transmission, our transmission model predicts that re-opening kindergartens and el-ementary schools could facilitate spread and lead to considerable excess COVID-19 a􀀁ributable deaths over a 90-day period. These findings indicate that targe􀀂ng interven􀀂ons to adults aged 20-49 are an important con-sidera􀀂on in hal􀀂ng resurgent epidemics, and preven􀀂ng COVID-19-a􀀁ributable deaths when kindergartens and elementary schools reopen.

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van Elsland S, Watson O, Alhaffar M, Mehchy Z, Whittaker C, Akil Z, Ainslie K, Baguelin M, Bhatt S, Boonyasiri A, Boyd O, Brazeau N, Cattarino L, Charles G, Ciavarella C, Cooper L, Coupland H, Cucunuba Perez Z, Cuomo-Dannenburg G, Djaafara A, Donnelly C, Dorigatti I, Eales O, van Elsland S, Nascimento F, Fitzjohn R, Flaxman S, Forna A, Fu H, Gaythorpe K, Green W, Hamlet A, Hauck K, Haw D, Hayes S, Hinsley W, Imai N, Jeffrey B, Johnson R, Jorgensen D, Knock E, Laydon D, Lees J, Mellan T, Mishra S, Nedjati Gilani G, Nouvellet P, Okell L, Olivera Mesa D, Pons Salort M, Ragonnet-Cronin M, Siveroni I, Stopard I, Thompson H, Unwin H, Verity R, Vollmer M, Volz E, Walters C, Wang H, Wang Y, Whittles L, Winskill P, Xi X, Ferguson N, Beals E, Walker P, Anonymous Authorset al., 2020, Report 31: Estimating the burden of COVID-19 in Damascus, Syria: an analysis of novel data sources to infer mortality under-ascertainment

The COVID-19 pandemic has resulted in substantial mortality worldwide. However, to date, countries in the Middle East and Africa have reported substantially lower mortality rates than in Europe and the Americas. One hypothesis is that these countries have been ‘spared’, but another is that deaths have been under-ascertained (deaths that have been unreported due to any number of reasons, for instance due to limited testing capacity). However, the scale of under-ascertainment is difficult to assess with currently available data. In this analysis, we estimate the potential under-ascertainment of COVID-19 mortality in Damascus, Syria, where all-cause mortality data has been reported between 25th July and 1st August. We fit a mathematical model of COVID-19 transmission to reported COVID-19 deaths in Damascus since the beginning of the pandemic and compare the model-predicted deaths to reported excess deaths. Exploring a range of different assumptions about under-ascertainment, we estimate that only 1.25% of deaths (sensitivity range 1% - 3%) due to COVID-19 are reported in Damascus. Accounting for under-ascertainment also corroborates local reports of exceeded hospital bed capacity. To validate the epidemic dynamics inferred, we leverage community-uploaded obituary certificates as an alternative data source, which confirms extensive mortality under-ascertainment in Damascus between July and August. This level of under-ascertainment suggests that Damascus is at a much later stage in its epidemic than suggested by surveillance reports, which have repo. We estimate that 4,340 (95% CI: 3,250 - 5,540) deaths due to COVID-19 in Damascus may have been missed as of 2nd September 2020. Given that Damascus is likely to have the most robust surveillance in Syria, these findings suggest that other regions of the country could have experienced similar or worse mortality rates due to COVID-19.

Report

Charniga K, Cucunubá ZM, Mercado M, Prieto F, Ospina M, Nouvellet P, Donnelly CAet al., 2020, Spatial and temporal invasion dynamics of the 2014-2017 Zika and chikungunya epidemics in Colombia

<jats:p>Zika virus (ZIKV) and chikungunya virus (CHIKV) were recently introduced into the Americas resulting in significant disease burdens. Understanding their spatial and temporal dynamics at the subnational level is key to informing surveillance and preparedness for future epidemics. We analyzed anonymized line list data on approximately 105,000 Zika virus disease and 412,000 chikungunya fever suspected and laboratory-confirmed cases during the 2014-2017 epidemics. We first determined the week of invasion in each city. Out of 1,122, 288 cities met criteria for epidemic invasion by ZIKA and 338 cities by CHIKV. We estimated that the geographic origin of both epidemics was located in Barranquilla, north Colombia. Using gravity models, we assessed the spatial and temporal invasion dynamics of both viruses to analyze transmission between cities. Invasion risk was best captured when accounting for geographic distance and intermediate levels of density dependence. Although a few long-distance invasion events occurred at the beginning of the epidemics, an estimated distance power of 1.7 (95% CrI: 1.5-2.0) suggests that spatial spread was primarily driven by short-distance transmission. Cities with large populations were more likely to spread disease than cities with smaller populations. Similarities between the epidemics included having the same estimated geographic origin and having the same five parameters estimated in the best-fitting models. ZIKV spread considerably faster than CHIKV.</jats:p>

Journal article

Lavezzo E, Franchin E, Ciavarella C, Cuomo-Dannenburg G, Barzon L, Del Vecchio C, Rossi L, Manganelli R, Loregian A, Navarin N, Abate D, Sciro M, Merigliano S, De Canale E, Vanuzzo MC, Besutti V, Saluzzo F, Onelia F, Pacenti M, Parisi S, Carretta G, Donato D, Flor L, Cocchio S, Masi G, Sperduti A, Cattarino L, Salvador R, Nicoletti M, Caldart F, Castelli G, Nieddu E, Labella B, Fava L, Drigo M, Gaythorpe KAM, Imperial College COVID-19 Response Team, Brazzale AR, Toppo S, Trevisan M, Baldo V, Donnelly CA, Ferguson NM, Dorigatti I, Crisanti Aet al., 2020, Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'., Nature, Vol: 584, Pages: 425-429, ISSN: 0028-0836

On the 21st of February 2020 a resident of the municipality of Vo', a small town near Padua, died of pneumonia due to SARS-CoV-2 infection1. This was the first COVID-19 death detected in Italy since the emergence of SARS-CoV-2 in the Chinese city of Wuhan, Hubei province2. In response, the regional authorities imposed the lockdown of the whole municipality for 14 days3. We collected information on the demography, clinical presentation, hospitalization, contact network and presence of SARS-CoV-2 infection in nasopharyngeal swabs for 85.9% and 71.5% of the population of Vo' at two consecutive time points. On the first survey, which was conducted around the time the town lockdown started, we found a prevalence of infection of 2.6% (95% confidence interval (CI) 2.1-3.3%). On the second survey, which was conducted at the end of the lockdown, we found a prevalence of 1.2% (95% Confidence Interval (CI) 0.8-1.8%). Notably, 42.5% (95% CI 31.5-54.6%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic (i.e. did not have symptoms at the time of swab testing and did not develop symptoms afterwards). The mean serial interval was 7.2 days (95% CI 5.9-9.6). We found no statistically significant difference in the viral load of symptomatic versus asymptomatic infections (p-values 0.62 and 0.74 for E and RdRp genes, respectively, Exact Wilcoxon-Mann-Whitney test). This study sheds new light on the frequency of asymptomatic SARS-CoV-2 infection, their infectivity (as measured by the viral load) and provides new insights into its transmission dynamics and the efficacy of the implemented control measures.

Journal article

Vecino-Ortiz AI, Villanueva Congote J, Zapata Bedoya S, Cucunuba ZMet al., 2020, Impact of contact tracing on COVID-19 mortality: An impact evaluation using surveillance data from Colombia.

<jats:p>BackgroundContact tracing is a key part of the public health surveillance toolkit. However, it is labor intensive and costly to carry it out. Some countries have faced challenges implementing contact tracing, and no impact evaluations to our knowledge have assessed its impact on COVID-19 mortality. This study assesses the impact of contact tracing in a middle-income country and provides data to support the expansion of contact tracing strategies with the aim of improving infection control. MethodsWe obtained publicly available data on all confirmed COVID-19 cases in Colombia between March 2 and June 16, 2020. (N=54,931 cases over 135 days of observation). We proxied contact tracing performance as the proportion of cases identified through contact tracing out of all cases identified, as suggested by WHO guidelines. We calculated the daily proportion of cases identified through contact tracing across 37 geographical units (32 departments and five districts). Further, we used a sequential log-log fixed-effects model to estimate the 21-days, 28-days, 42-days and 56-days lagged impact of the proportion of cases identified through contact tracing on the daily number of COVID-19 deaths. Both the proportion of cases identified through contact tracing and the daily number of COVID-19 deaths are smoothed using 7-day moving averages. Models control for prevalence of active cases, second-degree polynomials, and mobility indices. Robustness checks to include supply-side variables were performed.ResultsWe found that a 10 percent increase in the proportion of cases identified through contact tracing is related to COVID-19 mortality reductions between 0.8% and 3.4%. Our models explain between 47%-70% of the variance in mortality. Results are robust to changes of specification and inclusion of supply-side variables.ConclusionContact tracing is instrumental to contain infectious diseases and its prioritization as a surveillance strategy will have a substantial impact on

Journal article

Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, Whittaker C, Zhu H, Berah T, Eaton JW, Monod M, Perez Guzman PN, Schmit N, Cilloni L, Ainslie K, Baguelin M, Boonyasiri A, Boyd O, Cattarino L, Cucunuba Perez Z, Cuomo-Dannenburg G, Dighe A, Djaafara A, Dorigatti I, van Elsland S, Fitzjohn R, Gaythorpe K, Geidelberg L, Grassly N, Green W, Hallett T, Hamlet A, Hinsley W, Jeffrey B, Knock E, Laydon D, Nedjati Gilani G, Nouvellet P, Parag K, Siveroni I, Thompson H, Verity R, Volz E, Walters C, Wang H, Watson O, Winskill P, Xi X, Walker P, Ghani AC, Donnelly CA, Riley SM, Vollmer MAC, Ferguson NM, Okell LC, Bhatt Set al., 2020, Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe, Nature, Vol: 584, Pages: 257-261, ISSN: 0028-0836

Following the emergence of a novel coronavirus1 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions such as closure of schools and national lockdowns. We study the impact of major interventions across 11 European countries for the period from the start of COVID-19 until the 4th of May 2020 when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. We use partial pooling of information between countries with both individual and shared effects on the reproduction number. Pooling allows more information to be used, helps overcome data idiosyncrasies, and enables more timely estimates. Our model relies on fixed estimates of some epidemiological parameters such as the infection fatality rate, does not include importation or subnational variation and assumes that changes in the reproduction number are an immediate response to interventions rather than gradual changes in behavior. Amidst the ongoing pandemic, we rely on death data that is incomplete, with systematic biases in reporting, and subject to future consolidation. We estimate that, for all the countries we consider, current interventions have been sufficient to drive the reproduction number Rt below 1 (probability Rt< 1.0 is 99.9%) and achieve epidemic control. We estimate that, across all 11 countries, between 12 and 15 million individuals have been infected with SARS-CoV-2 up to 4th May, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions and lockdown in particular have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control.

Journal article

Unwin HJT, Mishra S, Bradley VC, Gandy A, Mellan TA, Coupland H, Ish-Horowicz J, Vollmer MAC, Whittaker C, Filippi SL, Xi X, Monod M, Ratmann O, Hutchinson M, Valka F, Zhu H, Hawryluk I, Milton P, Ainslie KEC, Baguelin M, Boonyasiri A, Brazeau NF, Cattarino L, Cucunuba Z, Cuomo-Dannenburg G, Dorigatti I, Eales OD, Eaton JW, van Elsland SL, FitzJohn RG, Gaythorpe KAM, Green W, Hinsley W, Jeffrey B, Knock E, Laydon DJ, Lees J, Nedjati-Gilani G, Nouvellet P, Okell L, Parag KV, Siveroni I, Thompson HA, Walker P, Walters CE, Watson OJ, Whittles LK, Ghani AC, Ferguson NM, Riley S, Donnelly CA, Bhatt S, Flaxman Set al., 2020, State-level tracking of COVID-19 in the United States, Publisher: Cold Spring Harbor Laboratory

<jats:title>Abstract</jats:title><jats:p>As of 1st June 2020, the US Centers for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly modelled the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We used changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. Nationally, we estimated 3.7% [3.4%-4.0%] of the population had been infected by 1st June 2020, with wide variation between states, and approximately 0.01% of the population was infectious. We also demonstrated that good model forecasts of deaths for the next 3 weeks with low error and good coverage of our credible intervals.</jats:p>

Working paper

Fu H, Xi X, Wang H, Boonyasiri A, Wang Y, Hinsley W, Fraser K, McCabe R, Olivera Mesa D, Skarp J, Ledda A, Dewe T, Dighe A, Winskill P, van Elsland S, Ainslie K, Baguelin M, Bhatt S, Boyd O, Brazeau N, Cattarino L, Charles G, Coupland H, Cucunuba Perez Z, Cuomo-Dannenburg G, Donnelly C, Dorigatti I, Green W, Hamlet A, Hauck K, Haw D, Jeffrey B, Laydon D, Lees J, Mellan T, Mishra S, Nedjati Gilani G, Nouvellet P, Okell L, Parag K, Ragonnet-Cronin M, Riley S, Schmit N, Thompson H, Unwin H, Verity R, Vollmer M, Volz E, Walker P, Walters C, Watson O, Whittaker C, Whittles L, Imai N, Bhatia S, Ferguson Net al., 2020, Report 30: The COVID-19 epidemic trends and control measures in mainland China

Report

Laiton-Donato K, Julián Villabona-Arenas C, Usme-Ciro JA, Franco-Muñoz C, Álvarez-Díaz DA, Villabona-Arenas LS, Echeverría-Londoño S, Cucunubá ZM, Franco-Sierra ND, Flórez AC, Ferro C, Ajami NJ, Walteros DM, Prieto F, Andrés Durán C, Ospina-Martínez ML, Mercado-Reyes Met al., 2020, Genomic epidemiology of SARS-CoV-2 in Colombia

<jats:title>Abstract</jats:title><jats:p>Coronavirus disease 2019 (COVID-19) was first diagnosed in Colombia from a traveler arriving from Italy on February 26, 2020. To date, available data on the origins and number or introductions of SARS-CoV-2 into the country are limited. Here, we sequenced SARS-CoV-2 from 43 clinical samples and—together with another 73 genomes sequences available from the country—we investigated the emergence and the routes of importation of COVID-19 into Colombia using epidemiological, historical air travel and phylogenetic observations. Our study provided evidence of multiple introductions, mostly from Europe, with at least 12 lineages being documented. Phylogenetic findings validated the linkage of epidemiologically-linked transmission chains. Our results demonstrate the advantages of genome sequencing to complement COVID-19 outbreak investigation and underscores that human mobility and genetic data are relevant to complete epidemiological investigation and better characterize virus transmission dynamics at local scales.</jats:p>

Journal article

Bhatia S, Imai N, Cuomo-Dannenburg G, Baguelin M, Boonyasiri A, Cori A, Cucunuba Perez Z, Dorigatti I, Fitzjohn R, Fu H, Gaythorpe K, Ghani A, Hamlet A, Hinsley W, Laydon D, Nedjati Gilani G, Okell L, Riley S, Thompson H, van Elsland S, Volz E, Wang H, Wang Y, Whittaker C, Xi X, Donnelly CA, Ferguson NMet al., 2020, Estimating the number of undetected COVID-19 cases among travellers from mainland China, Publisher: F1000 Research Ltd

Background: Since the start of the COVID-19 epidemic in late 2019, there have been more than 152 affected regions and countries with over 110,000 confirmed cases outside mainland China.Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries.Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that more than two thirds (70%, 95% CI: 54% - 80%, compared to Singapore; 75%, 95% CI: 66% - 82%, compared to multiple countries) of cases exported from mainland China have remained undetected.Conclusions: These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China.

Working paper

Walker PGT, Whittaker C, Watson OJ, Baguelin M, Winskill P, Hamlet A, Djafaara BA, Cucunubá Z, Olivera Mesa D, Green W, Thompson H, Nayagam S, Ainslie KEC, Bhatia S, Bhatt S, Boonyasiri A, Boyd O, Brazeau NF, Cattarino L, Cuomo-Dannenburg G, Dighe A, Donnelly CA, Dorigatti I, van Elsland SL, FitzJohn R, Fu H, Gaythorpe KAM, Geidelberg L, Grassly N, Haw D, Hayes S, Hinsley W, Imai N, Jorgensen D, Knock E, Laydon D, Mishra S, Nedjati-Gilani G, Okell LC, Unwin HJ, Verity R, Vollmer M, Walters CE, Wang H, Wang Y, Xi X, Lalloo DG, Ferguson NM, Ghani ACet al., 2020, The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries, Science, Vol: 369, Pages: 413-422, ISSN: 0036-8075

The ongoing COVID-19 pandemic poses a severe threat to public health worldwide. We combine data on demography, contact patterns, disease severity, and health care capacity and quality to understand its impact and inform strategies for its control. Younger populations in lower income countries may reduce overall risk but limited health system capacity coupled with closer inter-generational contact largely negates this benefit. Mitigation strategies that slow but do not interrupt transmission will still lead to COVID-19 epidemics rapidly overwhelming health systems, with substantial excess deaths in lower income countries due to the poorer health care available. Of countries that have undertaken suppression to date, lower income countries have acted earlier. However, this will need to be maintained or triggered more frequently in these settings to keep below available health capacity, with associated detrimental consequences for the wider health, well-being and economies of these countries.

Journal article

Nouvellet P, Bhatia S, Cori A, Ainslie K, Baguelin M, Bhatt S, Boonyasiri A, Brazeau N, Cattarino L, Cooper L, Coupland H, Cucunuba Perez Z, Cuomo-Dannenburg G, Dighe A, Djaafara A, Dorigatti I, Eales O, van Elsland S, Nscimento F, Fitzjohn R, Gaythorpe K, Geidelberg L, Grassly N, Green W, Hamlet A, Hauck K, Hinsley W, Imai N, Jeffrey B, Knock E, Laydon D, Lees J, Mangal T, Mellan T, Nedjati Gilani G, Parag K, Pons Salort M, Ragonnet-Cronin M, Riley S, Unwin H, Verity R, Vollmer M, Volz E, Walker P, Walters C, Wang H, Watson O, Whittaker C, Whittles L, Xi X, Ferguson N, Donnelly Cet al., 2020, Report 26: Reduction in mobility and COVID-19 transmission

In response to the COVID-19 pandemic, countries have sought to control transmission of SARS-CoV-2by restricting population movement through social distancing interventions, reducing the number ofcontacts.Mobility data represent an important proxy measure of social distancing. Here, we develop aframework to infer the relationship between mobility and the key measure of population-level diseasetransmission, the reproduction number (R). The framework is applied to 53 countries with sustainedSARS-CoV-2 transmission based on two distinct country-specific automated measures of humanmobility, Apple and Google mobility data.For both datasets, the relationship between mobility and transmission was consistent within andacross countries and explained more than 85% of the variance in the observed variation intransmissibility. We quantified country-specific mobility thresholds defined as the reduction inmobility necessary to expect a decline in new infections (R<1).While social contacts were sufficiently reduced in France, Spain and the United Kingdom to controlCOVID-19 as of the 10th of May, we find that enhanced control measures are still warranted for themajority of countries. We found encouraging early evidence of some decoupling of transmission andmobility in 10 countries, a key indicator of successful easing of social-distancing restrictions.Easing social-distancing restrictions should be considered very carefully, as small increases in contactrates are likely to risk resurgence even where COVID-19 is apparently under control. Overall, strongpopulation-wide social-distancing measures are effective to control COVID-19; however gradualeasing of restrictions must be accompanied by alternative interventions, such as efficient contacttracing, to ensure control.

Report

Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, Cuomo-Dannenburg G, Thompson H, Walker PGT, Fu H, Dighe A, Griffin JT, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunubá Z, FitzJohn R, Gaythorpe K, Green W, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Riley S, van Elsland S, Volz E, Wang H, Wang Y, Xi X, Donnelly CA, Ghani AC, Ferguson NMet al., 2020, Estimates of the severity of coronavirus disease 2019: a model-based analysis., Lancet Infectious Diseases, Vol: 20, Pages: 669-677, ISSN: 1473-3099

BACKGROUND: In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. METHODS: We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. FINDINGS: Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9-19·2) and to hospital discharge to be 24·7 days (22·9-28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for cen

Journal article

Dighe A, Cattarino L, Cuomo-Dannenburg G, Skarp J, Imai N, Bhatia S, Gaythorpe K, Ainslie K, Baguelin M, Bhatt S, Boonyasiri A, Boyd O, Brazeau N, Charles G, Cooper L, Coupland H, Cucunuba Perez Z, Djaafara A, Dorigatti I, Eales O, Eaton J, van Elsland S, Ferreira Do Nascimento F, Fitzjohn R, Flaxman S, Fraser K, Geidelberg L, Green W, Hallett T, Hamlet A, Hauck K, Haw D, Hinsley W, Jeffrey B, Knock E, Laydon D, Lees J, Mellan T, Mishra S, Nedjati Gilani G, Nouvellet P, Okell L, Parag K, Pons Salort M, Ragonnet-Cronin M, Thompson H, Unwin H, Verity R, Whittaker C, Whittles L, Xi X, Ghani A, Donnelly C, Ferguson N, Riley Set al., 2020, Report 25: Response to COVID-19 in South Korea and implications for lifting stringent interventions, 25

While South Korea experienced a sharp growth in COVID-19 cases early in the global pandemic, it has since rapidly reduced rates of infection and now maintains low numbers of daily new cases. Despite using less stringent “lockdown” measures than other affected countries, strong social distancing measures have been advised in high incidence areas and a 38% national decrease in movement occurred voluntarily between February 24th - March 1st. Suspected and confirmed cases were isolated quickly even during the rapid expansion of the epidemic and identification of the Shincheonji cluster. South Korea swiftly scaled up testing capacity and was able to maintain case-based interventions throughout. However, individual case-based contact tracing, not associated with a specific cluster, was a relatively minor aspect of their control program, with cluster investigations accounting for a far higher proportion of cases: the underlying epidemic was driven by a series of linked clusters, with 48% of all cases in the Shincheonji cluster and 20% in other clusters. Case-based contacts currently account for only 11% of total cases. The high volume of testing and low number of deaths suggests that South Korea experienced a small epidemic of infections relative to other countries. Therefore, caution is needed in attempting to duplicate the South Korean response in settings with larger more generalized epidemics. Finding, testing and isolating cases that are linked to clusters may be more difficult in such settings.

Report

Unwin H, Mishra S, Bradley VC, Gandy A, Vollmer M, Mellan T, Coupland H, Ainslie K, Whittaker C, Ish-Horowicz J, Filippi S, Xi X, Monod M, Ratmann O, Hutchinson M, Valka F, Zhu H, Hawryluk I, Milton P, Baguelin M, Boonyasiri A, Brazeau N, Cattarino L, Charles G, Cooper L, Cucunuba Perez Z, Cuomo-Dannenburg G, Djaafara A, Dorigatti I, Eales O, Eaton J, van Elsland S, Fitzjohn R, Gaythorpe K, Green W, Hallett T, Hinsley W, Imai N, Jeffrey B, Knock E, Laydon D, Lees J, Nedjati Gilani G, Nouvellet P, Okell L, Ower A, Parag K, Siveroni I, Thompson H, Verity R, Walker P, Walters C, Wang Y, Watson O, Whittles L, Ghani A, Ferguson N, Riley S, Donnelly C, Bhatt S, Flaxman Set al., 2020, Report 23: State-level tracking of COVID-19 in the United States

our estimates show that the percentage of individuals that have been infected is 4.1% [3.7%-4.5%], with widevariation between states. For all states, even for the worst affected states, we estimate that less than a quarter of thepopulation has been infected; in New York, for example, we estimate that 16.6% [12.8%-21.6%] of individuals have beeninfected to date. Our attack rates for New York are in line with those from recent serological studies [1] broadly supportingour choice of infection fatality rate.There is variation in the initial reproduction number, which is likely due to a range of factors; we find a strong associationbetween the initial reproduction number with both population density (measured at the state level) and the chronologicaldate when 10 cumulative deaths occurred (a crude estimate of the date of locally sustained transmission).Our estimates suggest that the epidemic is not under control in much of the US: as of 17 May 2020 the reproductionnumber is above the critical threshold (1.0) in 24 [95% CI: 20-30] states. Higher reproduction numbers are geographicallyclustered in the South and Midwest, where epidemics are still developing, while we estimate lower reproduction numbersin states that have already suffered high COVID-19 mortality (such as the Northeast). These estimates suggest that cautionmust be taken in loosening current restrictions if effective additional measures are not put in place.We predict that increased mobility following relaxation of social distancing will lead to resurgence of transmission, keepingall else constant. We predict that deaths over the next two-month period could exceed current cumulative deathsby greater than two-fold, if the relationship between mobility and transmission remains unchanged. Our results suggestthat factors modulating transmission such as rapid testing, contact tracing and behavioural precautions are crucial to offsetthe rise of transmission associated with loosening of social distancing. Overall, we

Report

Winskill P, Whittaker C, Walker P, Watson O, Laydon D, Imai N, Cuomo-Dannenburg G, Ainslie K, Baguelin M, Bhatt S, Boonyasiri A, Cattarino L, Ciavarella C, Cooper L, Coupland H, Cucunuba Perez Z, van Elsland S, Fitzjohn R, Flaxman S, Gaythorpe K, Green W, Hallett T, Hamlet A, Hinsley W, Knock E, Lees J, Mellan T, Mishra S, Nedjati Gilani G, Nouvellet P, Okell L, Parag K, Thompson H, Unwin H, Wang Y, Whittles L, Xi X, Ferguson N, Donnelly C, Ghani Aet al., 2020, Report 22: Equity in response to the COVID-19 pandemic: an assessment of the direct and indirect impacts on disadvantaged and vulnerable populations in low- and lower middle-income countries, 22

The impact of the COVID-19 pandemic in low-income settings is likely to be more severe due to limited healthcare capacity. Within these settings, however, there exists unfair or avoidable differences in health among different groups in society – health inequities – that mean that some groups are particularly at risk from the negative direct and indirect consequences of COVID-19. The structural determinants of these are often reflected in differences by income strata, with the poorest populations having limited access to preventative measures such as handwashing. Their more fragile income status will also mean that they are likely to be employed in occupations that are not amenable to social-distancing measures, thereby further reducing their ability to protect themselves from infection. Furthermore, these populations may also lack access to timely healthcare on becoming ill. We explore these relationships by using large-scale household surveys to quantify the differences in handwashing access, occupation and hospital access with respect to wealth status in low-income settings. We use a COVID-19 transmission model to demonstrate the impact of these differences. Our results demonstrate clear trends that the probability of death from COVID-19 increases with increasing poverty. On average, we estimate a 32.0% (2.5th-97.5th centile 8.0%-72.5%) increase in the probability of death in the poorest quintile compared to the wealthiest quintile from these three factors alone. We further explore how risk mediators and the indirect impacts of COVID-19 may also hit these same disadvantaged and vulnerable the hardest. We find that larger, inter-generational households that may hamper efforts to protect the elderly if social distancing are associated with lower-income countries and, within LMICs, lower wealth status. Poorer populations are also more susceptible to food security issues - with these populations having the highest levels under-nourishment whilst also being

Report

Vollmer MAC, Mishra S, Unwin HJT, Gandy A, Mellan TA, Bradley V, Zhu H, Coupland H, Hawryluk I, Hutchinson M, Ratmann O, Monod M, Walker P, Whittaker C, Cattarino L, Ciavarella C, Cilloni L, Ainslie K, Baguelin M, Bhatia S, Boonyasiri A, Brazeau N, Charles G, Cooper LV, Cucunuba Z, Cuomo-Dannenburg G, Dighe A, Djaafara B, Eaton J, van Elsland SL, FitzJohn R, Fraser K, Gaythorpe K, Green W, Hayes S, Imai N, Jeffrey B, Knock E, Laydon D, Lees J, Mangal T, Mousa A, Nedjati-Gilani G, Nouvellet P, Olivera D, Parag KV, Pickles M, Thompson HA, Verity R, Walters C, Wang H, Wang Y, Watson OJ, Whittles L, Xi X, Ghani AAM, Riley S, Okell LC, Donnelly CA, Ferguson NM, Dorigatti I, Flaxman S, Bhatt Set al., 2020, A sub-national analysis of the rate of transmission of COVID-19 in Italy

<jats:p>Italy was the first European country to experience sustained local transmission of COVID-19. As of 1st May 2020, the Italian health authorities reported 28,238 deaths nationally. To control the epidemic, the Italian government implemented a suite of non-pharmaceutical interventions (NPIs), including school and university closures, social distancing and full lockdown involving banning of public gatherings and non essential movement. In this report, we model the effect of NPIs on transmission using data on average mobility. We estimate that the average reproduction number (a measure of transmission intensity) is currently below one for all Italian regions, and significantly so for the majority of the regions. Despite the large number of deaths, the proportion of population that has been infected by SARS-CoV-2 (the attack rate) is far from the herd immunity threshold in all Italian regions, with the highest attack rate observed in Lombardy (13.18% [10.66%-16.70%]). Italy is set to relax the currently implemented NPIs from 4th May 2020. Given the control achieved by NPIs, we consider three scenarios for the next 8 weeks: a scenario in which mobility remains the same as during the lockdown, a scenario in which mobility returns to pre-lockdown levels by 20%, and a scenario in which mobility returns to pre-lockdown levels by 40%. The scenarios explored assume that mobility is scaled evenly across all dimensions, that behaviour stays the same as before NPIs were implemented, that no pharmaceutical interventions are introduced, and it does not include transmission reduction from contact tracing, testing and the isolation of confirmed or suspected cases. New interventions, such as enhanced testing and contact tracing are going to be introduced and will likely contribute to reductions in transmission; therefore our estimates should be viewed as pessimistic projections. We find that, in the absence of additional interventions, even a 20% return to pre-lockdown mob

Journal article

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